In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical
Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, from which the squared Euclidean distance is a particular case. Thus, the best suitable Bregman divergence for color quantization can be selected according to the input data. Moreover, the GHBNG yields a tree-structured model that represents the input data so that a hierarchical color quantization can be obtained, where each layer of the hierarchy contains a different color quantization
of the original image. Experimental results confirm the color quantization capabilities of this approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
